Scientometric study of patent literature in medicine
Bibliographic record
Abstract
A scientometric study was performed to assess the quantitative trend of patent literature in MEDLINE throughout 1965-2005. The kind of languages, publication type, journals, and the origin of published documents were presented. The study showed that the growth of patent literature in MEDLINE with an annual growth of 11.4% was 3.6 times higher than the common growth of the MEDLINE database which had an annual growth of 3.1% through 1965-2005.\nMore than 90% of all documents indexed as “patents” in MEDLINE were in English followed by Russian (4.12%), French (1.36%) and German (1.20%). The study indicated that Genes and Genetics was the most frequented Major MeSH Descriptors\n(Main Heading) in MEDLINE throughout the period of study.\nThe USA with publishing 55% of all documents indexed as patents in MEDLINE was the most prolific country in the term of patent literature, followed by England with 27%, USSR with 4%, Canada with 2%. It is remarkable that 82% of all\npublications belong to the USA and England; only 18% of publications belong to other countries in the world. The origin country of four documents stayed unknown (in MEDLINE). Journal “Nature” with publishing 14% of all\ndocuments, indexed as patents (patent literature) in PubMed was the most prolific periodical, followed by journal “Science” with 8%, “Nature-biotechnology” with 8%, “Lancet” with 2%, “BMJ” with 2%, “New Scientist” with 2% and “Food and drug law” with 1% respectively. From a total of 31 publications kind regarding to the documents indexed as patents in MEDLINE with a total frequencies of 3,207 titles, 46% of all publications were in the form of journal Articles, 22% in the form of News, 5% Letter, 5% Comment, 4% Review, 3% Editorial, 2% Newspaper Article, 2% Research Support, 2% English Abstract. The rest were less than 2%.\nThe proportion of publications in English showed considerable growth through 1965-2005. It reached from 52% in 1965 to 90% in 2005 an increase of 72%. Analysis of study\npredicted that the percentage of publications in English in MEDLINE will reach to the saturation level at 97% in 2030. This indicates that the editorial policy of entering data to the database of MEDLINE is being changed, and the atention\nof policy makers in this database have focused on the literature of science in English.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".